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Nonparametric clustering of discrete probability distributions with generalized Shannon’s entropy and heatmap

Author

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  • Zhang, Jialin
  • Shi, Jingyi

Abstract

This article proposes a non-parametric clustering heatmap for non-ordinal samples based on their underlying discrete probability distributions. The proposed heatmap is a preliminary tool to extract immediate information from non-ordinal data without making any parametric assumption.

Suggested Citation

  • Zhang, Jialin & Shi, Jingyi, 2024. "Nonparametric clustering of discrete probability distributions with generalized Shannon’s entropy and heatmap," Statistics & Probability Letters, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:stapro:v:208:y:2024:i:c:s0167715224000397
    DOI: 10.1016/j.spl.2024.110070
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    References listed on IDEAS

    as
    1. Zhiyi Zhang & Michael Grabchak, 2016. "Entropic representation and estimation of diversity indices," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(3), pages 563-575, September.
    2. Zhiyi Zhang, 2020. "Generalized Mutual Information," Stats, MDPI, vol. 3(2), pages 1-8, June.
    3. Zhiyi Zhang & Chen Chen & Jialin Zhang, 2020. "Estimation of population size in entropic perspective," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(2), pages 307-324, January.
    Full references (including those not matched with items on IDEAS)

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